Design of a Bidirectional Veneer Defect Repair Method Based on Parametric Modeling and Multi-Objective Optimization

To meet the high- performance requirements of new energy vehicle drive, the optimization design of 8/6 Switched Reluctance Motor is realized based on finite element parametric modeling of the motor. Firstly, the initial design of motor structure parameters is carried out based on the mathematical model of Switched Reluctance Motor, and the simulation model of the motor is built using RMxprt platform, and the debugging of the characteristics of the wide speed range of the motor is finished. Then, the parametric finite element model of the motor is generated, and the stator and rotor pole arc coefficients of the motor are selected as the optimization variables, and the multi-objective compromise optimization of the torque characteristics and efficiency of the motor is carried out by using the Quasi-Newton method weighting method. Finally, the magnetic field distribution, torque characteristics, efficiency and speed range characteristics before and after optimization are compared, proving that the optimized Switch Reluctance Motor can achieve multi-objective performance optimization. The motor designed by this modeling optimization method can improve the requirements of vehicle driving better.

Development of knowledge based parametric CAD modeling system for spur gear: An approach

The lightweight design of vehicle components is regarded as a complex optimization problem, which usually needs to achieve two or more optimization objectives. It can be firstly solved by a multi-objective optimization algorithm for generating Pareto solutions, before then seeking the optimal design. However, it is difficult to determine the optimal design for lack of engineering knowledge about ideal and nadir values. Therefore, this paper proposes a multi-objective optimization procedure combined with the NSGA-II algorithm with entropy weighted TOPSIS for the lightweight design of the dump truck carriage. The finite element model of the dump truck carriage was firstly developed for modal analysis under unconstrained free state and strength analysis under the full load and lifting conditions. On this basis, the multi-objective lightweight optimization of the dump truck carriage was carried out based on the Kriging surrogate model and the NSGA-II algorithm. Then, the entropy weight TOPSIS method was employed to select the optimal design of the dump truck from Pareto solutions. The results show that the optimized dump truck carriage achieves a remarkable mass reduction of 81 kg, as much as 3.7%, while its first-order natural frequency and strength performance are slightly improved compared with the original model. Accordingly, the proposed procedure provides an effective way for vehicle lightweight design.

This chapter seeks to determine the optimal structural design parameter values for a 6-story controlled rocking steel braced frame (CRSBF) building that minimizes the upfront (initial construction) and earthquake-induced economic and environmental impacts. The dead load on the rocking frame, initial post-tensioning force, fuse strength and frame aspect ratio are the considered parameters. Earthquake-induced economic losses are assessed using the FEMA P58 methodology and the Economic Input-Output Life Cycle Assessment is used to quantify greenhouse gas emissions associated with initial construction and repair and replacement activities following earthquake damage. Surrogate models are developed as compact representations of the statistical relationship between the structural design parameters and economic and environmental impacts. Once validated, the surrogate models are used to perform single- and multi-objective optimization using the desirability function approach. Differences in the sensitivity of the two impact categories (environmental and economic) to variations in the individual structural design parameters are also highlighted.

With the growing proportion of clean energy in integrated energy systems (IES), energy supply uncertainty and spatial-temporal dispersion are becoming increasingly prevalent. System modeling and optimal scheduling are facing greater challenges. In this paper, we improve the non-dominated sorting genetic algorithm (NSGA-II) to address the above problems and propose a two-stage multi-objective benefit-equilibrium optimization coordination of the electric-thermal coupled integrated energy system. Firstly, this paper carries out the thermodynamic characteristics analysis of the equipment components of the electro-thermal coupled energy system, which reflects the structural features of the system, the performance of each equipment under different task conditions, and the mechanism of the system; based on the above characteristic analysis, a two-stage multi-objective optimization of electro-thermal coupled system optimization coordination is proposed to establish the objective function and carry out each objective balance constraint; the NSGA-II algorithm is as well as improved. According to the operation stage, operation generation and the NSGA-II algorithm are improved by dynamically adjusting the operating parameters of evolving individuals of the operation stage, operational generation, and the number of undominated individuals in the current temporary population. By making the algorithm adaptation to improve the adaptive capacity of the evolution operator, we solve the two-step model and obtain the Pareto optimal front for each energy device. In summary, the results of the analysis of the IES under the coupling of power system and thermal system show that the constructed model and the proposed algorithm can effectively improve the accuracy of the renewable energy system and the optimization decision. The results of the research further reflect the benefits of the proposed multi-objective optimization scheme in accounting for economic, renewable energy, and complex operating constraints which ensure the economical and stable operation of the system, as well as the robustness of optimal scheduling.

This study focuses on investigating different multi-objective functions for the short-term and the long-term waterflood management applied in Brugge field benchmark model using NSGA-II algorithm. The short-term waterflood management is defined using four time-steps for two-year period with total of 120 decision variables. Three multi-objective function optimization cases are investigated for the short-term study [i.e., maximizing total oil production and minimizing total water production (Case-1), maximizing total oil production and minimizing total water cut (Case-2) and maximizing total oil production, maximizing net present value (NPV) and minimizing total water cut (Case-3)]. The results show that the highest oil production obtained from the Pareto front is in Case-1 with small difference compared to the other two Cases. The highest NPV is also achieved in Case-1 because of lower water production and lower water injection compared to Case-2 and Case-3. Long-term multi-objective optimization study (Case-4) is then run using NSGA-II algorithm for ten years with 640 decision variables considering well completions control in the producers and the injection rates control in injectors. The Pareto optimal solutions obtained by NSGA-II algorithm have shown higher NPV results with 30% improvement compared to previous work (Foroud et al. in J Petrol Sci Eng 167:131–151, 2018). The study has demonstrated the convergence into different Pareto optimal solutions obtained by applying NSGA-II algorithm coupled with the reservoir simulation model for the different optimization cases using Brugge field benchmark model. The results obtained deliver good range of optimum conditions from which any appropriate operating solution can be selected based on the requirements of the decision maker.

Lightweight design is one of the important ways to reduce automobile fuel consumption and exhaust emissions. At the same time, the fatigue life of automobile parts also greatly affects vehicle safety. This paper proposes a multi-objective reliability optimization method by integrating Monte Carlo simulation (MCS) with the NSGA-II algorithm coupled with entropy weighted grey relational analysis (GRA) for lightweight design of the lower control arm of automobile Macpherson suspension. The dynamic load histories of the control arm were extracted through dynamic simulations of a rigid-flexible coupling vehicle model on virtual proving ground. Then, the nominal stress method was used to predict its fatigue life. Six design variables were defined to describe the geometric dimension of the control arm, while mass and fatigue life were taken as optimization objectives. The multi-objective optimization design of the control arm was carried out based on the Kriging surrogate model and NSGA-II algorithm. Aiming at the uncertainty of design variables, the reliability constraint was added to the multi-objective optimization to improve the reliability of the fatigue life of the control arm. The optimal design of the control arm was determined from Pareto solutions by entropy weighted grey relational analysis (GRA). The optimization results show that the mass of the control arm was reduced by 4.1% and the fatigue life was increased by 215.8% while its reliability increased by 7.8%. The proposed multi-objective reliability optimization method proved to be feasible and effective for lightweight design of a suspension control arm.

To improve the performance of a solid rocket motor (SRM), a multiobjective optimal design method that can consider the structural integrity, internal ballistic performance, and loading performance of the SRM was proposed based on parametric modeling and surrogate modeling technology. Firstly, the parametric modeling technology was introduced into the field of structural integrity analysis for a high-loading SRM, based on which the influences of load and geometric parameters on the maximum von Mises strain of the SRM grain were analyzed, which effectively improved the sampling speed and prediction accuracy of the surrogate model. Combining the calculation models of the combustion surface area and volume loading fraction of the SRM, the Pareto optimal solution set was obtained based on the NSGA-II algorithm. Under the constraints of the optimization model, the maximum von Mises strain can be reduced by up to 26.72% and the volume loading fraction can be increased by up to 1.83% compared with the original. In addition, the optimal design method proposed in this paper is significantly superior in efficiency, capable of reducing both the single sampling time by more than 95% and the number of numerical simulations from 20,000 to 400, and the average prediction deviation is only 1.87%.

This study focuses on optimizing the foundation pit dewatering scheme using the foundation pit dewatering theory and the principles of multi-objective optimization. It explores the development of a multi-objective optimization model and efficient solution technology for foundation pit dewatering. This research focuses on the foundation pit dewatering project at the inverted siphon section of Xixiayuan canal head, specifically from pile number XZ0+326 to XZ0+500. It establishes an optimized mathematical model for foundation pit dewatering that incorporates three objectives. Additionally, a dewatering optimization program is developed by utilizing the MATLAB optimization toolbox and the multi-objective optimization algorithm program based on the NSGA-II algorithm (Gamultiobj). The multi-objective optimization mathematical model is solved, and a Pareto-optimal solution set with uniform distribution is obtained. The multi-objective optimization evaluation system based on AHP is constructed from the three aspects of dewatering cost, the impact of settlement on the environment, and the safety and stability of the foundation pit. The optimization scheme of the Pareto-optimal solution set is selected as the decision result to provide multiple feasible schemes for the dewatering construction of foundation pits. The optimization scheme is verified by using the GMS software. The simulation results demonstrate that the optimization scheme fulfills the requirements for water level and settlement control. Moreover, the developed optimization program efficiently solves the multi-objective optimization problem associated with foundation pit dewatering. Lastly, an evaluation system incorporating the NSGA-II algorithm and AHP is developed and utilized in the context of dewatering engineering in order to offer multiple viable optimal dewatering schemes.

Usual CAE tools simulate the behavior of composite parts from models considering the structures as being homogenized. Such approach reveals itself not to be effective when the engineer aims at determining the number of plies and the material characteristics of each ply to aim a specific dynamic behavior. To reply to this problem, we developed a multi-scale model that explicitly integrates the different design parameters of the composite structure being considered at different scales: the number of plies, the orthotropic law of each ply and the characteristics of each interface between the plies made by the matrix. This paper is detailing the method that we developed to lead to our multi-scale and parametric model. This method is coupled to an experimental approach that allows specific variables named fractional variables to be identified. These variables add to the detailed representation of the dynamic capacities of the laminated composite beams that led our study. In the case of our composite beams, the effect of damping due to the ply-interface behavior is significant, and consequently we dealt with the viscoelastic response of the laminated composite beam under dynamic load. As a result, the strategy of simulation based on our reduced, viscoelastic and multi-scale beam model is presented: solutions with low computational resources may be obtained.

As a core problem of multi-agent systems, multiagent pathfinding has an important impact on the efficiency of multi-agent systems. Because of this, many novel multi-agent pathfinding methods have been proposed over the years. However, these methods have focused on different agents with different goals for research, and less research has been done on scenarios where different agents have the same goal. We propose a multiagent pathfinding method incorporating a multi-objective gray wolf optimization algorithm to solve the multi-agent pathfinding problem with the same objective. First, constrained optimization modeling is performed to obtain objective functions about agent wholeness and security. Then, the multi-objective gray wolf optimization algorithm is improved for solving the constrained optimization problem and further optimized for scenarios with insufficient computational resources. To verify the effectiveness of the multi-objective gray wolf optimization algorithm, we conduct experiments in a series of simulation environments and compare the improved multi-objective grey wolf optimization algorithm with some classical swarm intelligence optimization algorithms. The results show that the multi-agent pathfinding method incorporating the multi-objective gray wolf optimization algorithm is more efficient in handling multi-agent pathfinding problems with the same objective.

A well-designed battery thermal management system (BTMS) can achieve optimal cooling performance with less power consumption than a poorly-designed system. However, it is difficult to use the computational fluid dynamics (CFD) method to perform an effective and optimal design of BTMSs when there are several structural design parameters and multiple evaluation criteria. In this paper, instead of CFD, a compound surrogate model based on the mixture of experts (MoE) method is developed to accurately approximate the BTMS performance of different structural configurations. Then, the multiple criteria evaluation of the structural design is transformed into a multiobjective optimization (MOO) problem, which is solved by the nondominated sorting genetic algorithm II (NSGA-II). To address the nonuniqueness of the optimal solutions and the contradiction between evaluation criteria, the entropy weight method (EWM) and criteria importance through the intercriteria correlation (CRITIC) method are applied to analyze the weight of each evaluation criterion. Finally, the optimal structural parameters are obtained for the corresponding weights. The results show that the surrogate-based MOO can find a structural design that meets expectations, and this approach can provide guidelines for the design of BTMSs.

The lightweight design of the automotive front subframe was performed by combining multi-condition topology optimization and multi-objective optimization approaches. Firstly, to avoid the singularity of the research condition, a rigid-flexible coupled multibody dynamic model of the front suspension was established to extract the loads at articulation points of the subframe under typical working conditions. Then, a finite element model of the front subframe was constructed for strength and modal analysis. Multi-condition topology optimization of the front subframe envelope was performed utilizing the compromise programming approach. The subframe was redesigned according to the optimization results to establish an explicit parametric model, which avoided the blindness of optimization. Three approximate models were established through the optimal Latin hypercube test method to improve the efficiency of subsequent optimization. Furthermore, three algorithms were employed for multi-objective optimization based on the approximate model with the highest accuracy. To enhance the reliability of the optimization results, a comparison of the lightweighting effect of the three algorithms was performed, and the NSGA-II algorithm was determined as the final algorithm due to its greater effectiveness in lightweighting. The optimized front subframe has met various performance specifications while increasing the first-order frequency by 10 Hz and reducing weight by 3.27 kg.

The characteristics of military equipment maintenance work are analyzed. According to the actual needs of the army, the optimization objective is designed, and a multiobjective flexible maintenance process optimization model is built based on the maintenance business organization process. Combining the advantages of NSGA-II algorithm and the simulated annealing algorithm, this paper proposes a novel improved HNSGSA algorithm, of which algorithm flow is detailed. In accordance with the requirements of the optimization model, this paper also specifically designs the coding methods of the process sequence, the equipment selection and the process scheduling, and the corresponding cross mutation method. The feasibility of the built model is verified by the actual data of maintenance business. And, the superiority, accuracy, and effectiveness of the proposed algorithm are further validated by the comparison with the NSGA-II algorithm and the simulated annealing algorithm, providing a scientific reference for the army to carry out equipment maintenance.

In order to optimize the local search efficiency of multi-objective parameters of flux switching permanent motor based on traditional NSGA-II algorithm, an improved NSGA-II (iNSGA-II) algorithm is proposed, with an anti-redundant mutation operator and forward comparison operation designed for quick identification of non-dominated individuals. In the initial stage of the iNSGA-II algorithm, half of the individual populations were randomly generated, while the other half was generated according to feature distribution information. Taking the flux switching permanent motor stator/rotor gap, permanent magnets width, stator tooth width, rotor tooth width and other parameters as optimization variables, the flux switching permanent motor maximum output shaft torque and minimum torque ripple are taken as optimization objectives, thus a multi-objective optimization model is established. Real number coding was adopted for obtaining the Pareto optimal solution of flux switching permanent motor structure parameters. The results showed that the iNSGA-II algorithm is better than the traditional NSGA-II on convergence. A 1.8L TOYOTA PRIUS model was selected as the prototype vehicle. By using the optimized parameters, a joint optimization simulation model was established by calling ADVISOR’s back-office function. The simulation results showed that the entire vehicle’s 100-km acceleration time is under 8 s and the battery’s SOC value maintains at 0.5–0.7 in the entire cycle, implying that the iNSGA-II algorithm optimizes the flux switching permanent motor design and is suitable for the initial design and optimizing calculation of the flux switching permanent motor.